A conceptual framework for prognostic research

Abstract

Background: Prognostic research has many important purposes, including (i) describing the natural history and clinical course of health conditions, (ii) investigating variables associated with health outcomes of interest, (iii) estimating an individual's probability of developing different outcomes, (iv) investigating the clinical application of prediction models, and (v) investigating determinants of recovery that can inform the development of interventions to improve patient outcomes. But much prognostic research has been poorly conducted and interpreted, indicating that a number of conceptual areas are often misunderstood. Recent initiatives to improve this include the Prognosis Research Strategy (PROGRESS) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Statement. In this paper, we aim to show how different categories of prognostic research relate to each other, to differentiate exploratory and confirmatory studies, discuss moderators and mediators, and to show how important it is to understand study designs and the differences between prediction and causation. Main text: We propose that there are four main objectives of prognostic studiesdescription, association, prediction and causation. By causation, we mean the effect of prediction and decision rules on outcomes as determined by intervention studies and the investigation of whether a prognostic factor is a determinant of outcome (on the causal pathway). These either fall under the umbrella of exploratory (description, association, and prediction model development) or confirmatory (prediction model external validation and investigation of causation). Including considerations of causation within a prognostic framework provides a more comprehensive roadmap of how different types of studies conceptually relate to each other, and better clarity about appropriate model performance measures and the inferences that can be drawn from different types of prognostic studies. We also propose definitions of 'candidate prognostic factors', 'prognostic factors', 'prognostic determinants (causal)' and 'prognostic markers (non-causal)'. Furthermore, we address common conceptual misunderstandings related to study design, analysis, and interpretation of multivariable models from the perspectives of association, prediction and causation. Conclusion: This paper uses a framework to clarify some concepts in prognostic research that remain poorly understood and implemented, to stimulate discussion about how prognostic studies can be strengthened and appropriately interpreted.

The strength of association between psychological factors and clinical outcome in tendinopathy: A systematic review

“…In contrast to cross-sectional studies, longitudinal studies can evaluate whether psychological factors predict disease outcome over time. Studies of prediction require prospectively collected longitudinal data where the outcome is not present at enrolment [ 53 ]. This temporal sequence is important in determining cause and effect [ 54 ].…”

Building models for prediction: are we good at it?

“…This definition encompasses a clear consideration about timing of events for measuring predictors and outcomes. Prognostic modelling studies are intrinsically longitudinal, and models are built on data where the predictors precede the outcomes and where the outcomes are not present at enrolment [4]. Patients or participants that are included in the study should be at risk to develop the outcome of interest.…”

“…Preferably, the sample is heterogeneous, including a wide range of values for the predictor variables. The best design for a prognostic modelling study is a prospective cohort [4,5]. Developing a prediction model does not end with its construction.…”

“…Kent et al reported from their experience that there are often conceptual misunderstandings about what can be inferred from a multivariable model from the perspectives of association, prediction, and causation [4]. When building and testing a model, we should ask ourselves what do we already know and what question are we trying to answer?…”

“…The prognostic modelling strategies will depend on the type of research question we are asking, and this will, in turn, drive the interpretation of findings. The framework developed by Kent et al [4], by clarifying the conceptual positioning of the researcher, should help strengthen prognostic study design and interpretation.…”